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Related Experiment Video

Updated: Sep 12, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Deep learning and digital twin integration for structural damage detection in ancient pagodas.

Sida Xu¹, Haonan Chen²

¹College of Humanities and Arts, Macau University of Science and Technology, Macau, 999078, China.

Scientific Reports

|August 4, 2025

Summary

This study introduces a digital twin and improved YOLO algorithm method for detecting structural damage in cultural heritage buildings. This approach enhances accuracy and efficiency while reducing costs compared to manual methods.

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Area of Science:

Artificial Intelligence
Digital Twin Technology
Cultural Heritage Preservation

Background:

Structural damage to cultural heritage buildings threatens their integrity.
Existing deep learning methods struggle with full coverage and multi-angle observation of architectural sculptures.
Limitations include substantial detection errors in current object detection technologies.

Purpose of the Study:

To propose a novel detection method integrating digital modeling with an improved YOLO algorithm.
To enable full-angle and multi-seasonal scene transformations for damage assessment.
To address limitations in current deep learning-based object detection for architectural heritage.

Main Methods:

Generating digital twin models of architectural scenes through drone-based panoramic scanning.
Utilizing an improved YOLO algorithm for damage detection under various environmental conditions.
Employing evaluation metrics for automated analysis of detection accuracy and damage extent.

Main Results:

The proposed method enables full-angle and multi-seasonal scene transformations.
Detection performance was evaluated under varying weather and lighting conditions.
The technology significantly reduces labor costs and improves detection accuracy and efficiency.

Conclusions:

The integrated digital twin and improved YOLO algorithm offers a highly effective solution for assessing damage in historical buildings.
This approach provides a reliable technical solution for cultural heritage preservation.
Automated detection in digitalized scenarios surpasses traditional manual measurement methods.